A self-driving laboratory designed to accelerate the discovery of adhesive materials

Rooney, Michael B.; MacLeod, Benjamin P.; Oldford, Ryan; Thompson, Zachary; White, Kolby L.; Tungjunyatham, Justin; Stankiewicz, Brian J.; Berlinguette, Curtis P.

doi:10.1039/d2dd00029f

Cited by 24 publications

(19 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In material sciences, Macleod et al used the modular robotic platform Ada capable of autonomously optimizing the hole mobility of the materials commonly used in perovskite solar cells and consumer electronics [11], as well as discovering new synthesis conditions for optimized conductivities and processing temperatures for palladium films [12]. Robotics coupled with Bayesian optimization were used in multiple cases: autonomous synthesis and resistance minimization of thin films [13], optimizing mechanical properties of structures for a given application [14], improving adhesive formulations [15], achieving targeted 3D print features in additive manufacturing [16], discovering novel battery electrolytes [17], and search for photocatalyst mixtures with improved activity for hydrogen production from water [18].…”

Section: Figurementioning

confidence: 99%

Perspectives for self-driving labs in synthetic biology

Martín¹,

Radivojević²,

Zucker³

et al. 2023

Current Opinion in Biotechnology

View full text Add to dashboard Cite

Section: Figurementioning

confidence: 99%

Perspectives for self-driving labs in synthetic biology

Martín¹,

Radivojević²,

Zucker³

et al. 2023

Current Opinion in Biotechnology

View full text Add to dashboard Cite

“…In the last few years, great successes have been booked with self-driving laboratories (see Melnikov et al 2018;Du et al 2021;Erps et al 2021;Rooney et al 2022;Gongora et al 2020;Bennett and Abolhasani 2022). These laboratories differ from automated experimental set-ups in that they form a closed-loop: they can often run by themselves for days-if not weeks-without any human intervention (Soldatov et al, 2021, p. 619).…”

Section: The Role Of Ai-systems In Basic Researchmentioning

confidence: 99%

Introducing a four-fold way to conceptualize artificial agency

Lier

2023

Synthese

View full text Add to dashboard Cite

Recent developments in AI-research suggest that an AI-driven science might not be that far off. The research of for Melnikov et al. (2018) and that of Evans et al. (2018) show that automated systems can already have a distinctive role in the design of experiments and in directing future research. Common practice in many of the papers devoted to the automation of basic research is to refer to these automated systems as ‘agents’. What is this attribution of agency based on and to what extent is this an important notion in the broader context of an AI-driven science? In an attempt to answer these questions, this paper proposes a new methodological framework, introduced as the Four-Fold Framework, that can be used to conceptualize artificial agency in basic research. It consists of four modeling strategies, three of which were already identified and used by Sarkia (2021) to conceptualize ‘intentional agency’. The novelty of the framework is the inclusion of a fourth strategy, introduced as conceptual modeling, that adds a semantic dimension to the overall conceptualization. The strategy connects to the other strategies by modeling both the actual use of ‘artificial agency’ in basic research as well as what is meant by it in each of the other three strategies. This enables researchers to bridge the gap between theory and practice by comparing the meaning of artificial agency in both an academic as well as in a practical context.

show abstract

“…This leads to the necessity to represent these workflows in extremely high granularity. The supplementary data generated by publications regarding automated labs show the level of detail that is needed to achieve automation [21,48,49] . Another important aspect of data management in automated labs is tracking the process itself in real-time [16] .…”

Section: Data Modelmentioning

confidence: 99%

Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations

2023

J Mater Inf

View full text Add to dashboard Cite

The escalating adoption of high-throughput methods in applied materials science dramatically increases the amount of generated data and allows for the deployment and use of sophisticated data-driven methods. To exploit the full potential of these accelerated approaches, the generated data need to be managed, preserved and shared. The heterogeneity of such data calls for highly flexible models to represent the data from fabrication workflows, measurements and simulations. We propose the use of a native graph database to store the data instead of relying on rigid relational data models. To develop a flexible and extendable data model, we create an ontology that serves as the blueprint of the data model. The Python framework Django is used to enable seamless integration into the virtual materials intelligence platform VIMI. The Django framework relies on the Object Graph Mapper neomodel to create a mapping between database classes and Python objects. The model can store the whole bandwidth of the data from fabrication to simulation data. Implementing the database into a platform will encourage researchers to share data while profiting from rich and highly curated data to accelerate their research.

show abstract

A self-driving laboratory designed to accelerate the discovery of adhesive materials

Abstract: This self-driving laboratory combines a robot for preparing and testing adhesive bonds with an optimizer to rapidly improve adhesive formulations.

Cited by 24 publications

References 18 publications

Perspectives for self-driving labs in synthetic biology

Perspectives for self-driving labs in synthetic biology

Introducing a four-fold way to conceptualize artificial agency

Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations

Contact Info

Product

Resources

About